Intuitively, we need to access language model to improve the image clustering with the semantic information. Some works [1]}, [2]}, [3]} try to explore the image-caption pairs to cluster images, but constructing the images with qualified captions is cost-intensive in real applications. Note that the great success of visual-language pre-training model such as CLIP [4]}, which is trained on a dataset of 400 million image-text pairs available on the internet to align texts and images in a common feature space by capturing the image-text relationships. It has shown surprising results in zero-shot learning tasks. However, we need to know the class names in zero-shot task, which hinders some potential applications such as image clustering where the class names are unavailable. This motivates us to utilize the visual-language pre-training model to compensate the semantic information for better image clustering.
